import os import csv def detLabel(filename): #determine the label of the recording if "cello" in filename: label = 0 elif "church" in filename: label = 1 elif "clarinet" in filename: label = 2 elif "flute" in filename: label = 3 elif "guitar" in filename: label = 4 elif "harp" in filename: label = 5 elif "marimba" in filename: label = 6 elif "perldrop" in filename: label = 7 elif "piano" in filename: label = 8 elif "synlead3" in filename: label = 9 else: #violin label = 10 return label fileOrigin = "samples" dataFile = "data.csv" with open(dataFile, 'w', newline='') as csvfile: fileWriter = csv.writer(csvfile, delimiter=',') fileWriter.writerow(['filename', 'instrument']) for filename in os.listdir(fileOrigin): fileWriter.writerow([filename, detLabel(filename)])

这段代码的作用是将文件夹samples中的音频文件按照乐器种类的不同标记，并将它们的名称和标记存储在名为data.csv的CSV文件中。

首先，定义了一个名为detLabel的函数，用于根据文件名确定音频文件所属的乐器种类，并返回一个标记（从0到10）。其中，0~9分别代表cello、church、clarinet、flute、guitar、harp、marimba、perldrop、piano和synlead3，10代表violin。

然后，代码定义了两个变量fileOrigin和dataFile，分别表示存放音频文件的文件夹路径和CSV文件的路径。

接下来，使用Python内置的os模块中的listdir()函数遍历fileOrigin文件夹中的所有文件名，并将它们传递给detLabel函数，获取对应的标记。最后，将文件名和标记写入data.csv文件中，以逗号分隔。其中，第一行为文件头，包括两列：filename和instrument。

相关问题

def detSong(filename): #determine the label of the recording if "babillarde" in filename: song = 0 elif "let" in filename: song = 1 elif "sea" in filename: song = 2 elif "styrienne" in filename: song = 3 else: # tarantella song = 4 return song samplesOrigin = "samples" songsOrigin = "Recordings" numLabels = 11 numSongs = 5 numFiles = {} lengthInstr = {} lengthSong = {} hitsSong = {}、

这段代码定义了一个名为detSong的函数，用于确定音频文件的标签（乐器类别）。具体来说，函数接受一个参数filename，表示音频文件的名称，然后根据文件名中的关键字来确定标签。如果文件名中包含"babillarde"，则标签为0；如果包含"let"，则标签为1；如果包含"sea"，则标签为2；如果包含"styrienne"，则标签为3；否则标签为4。最后，函数返回确定的标签。

接下来，代码定义了一些变量：samplesOrigin表示采样数据的路径，songsOrigin表示音频文件的路径，numLabels表示标签的数量（这里是11，但是代码中没有使用），numSongs表示音频文件的数量，numFiles、lengthInstr、lengthSong和hitsSong都是空字典，用于存储后续处理过程中的一些信息。

import numpy as np def sigmoid(x): # the sigmoid function return 1/(1+np.exp(-x)) class LogisticReg(object): def __init__(self, indim=1): # initialize the parameters with all zeros # w: shape of [d+1, 1] self.w = np.zeros((indim + 1, 1)) def set_param(self, weights, bias): # helper function to set the parameters # NOTE: you need to implement this to pass the autograde. # weights: vector of shape [d, ] # bias: scaler def get_param(self): # helper function to return the parameters # NOTE: you need to implement this to pass the autograde. # returns: # weights: vector of shape [d, ] # bias: scaler def compute_loss(self, X, t): # compute the loss # X: feature matrix of shape [N, d] # t: input label of shape [N, ] # NOTE: return the average of the log-likelihood, NOT the sum. # extend the input matrix # compute the loss and return the loss X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1) # compute the log-likelihood def compute_grad(self, X, t): # X: feature matrix of shape [N, d] # grad: shape of [d, 1] # NOTE: return the average gradient, NOT the sum. def update(self, grad, lr=0.001): # update the weights # by the gradient descent rule def fit(self, X, t, lr=0.001, max_iters=1000, eps=1e-7): # implement the .fit() using the gradient descent method. # args: # X: input feature matrix of shape [N, d] # t: input label of shape [N, ] # lr: learning rate # max_iters: maximum number of iterations # eps: tolerance of the loss difference # TO NOTE: # extend the input features before fitting to it. # return the weight matrix of shape [indim+1, 1] def predict_prob(self, X): # implement the .predict_prob() using the parameters learned by .fit() # X: input feature matrix of shape [N, d] # NOTE: make sure you extend the feature matrix first, # the same way as what you did in .fit() method. # returns the prediction (likelihood) of shape [N, ] def predict(self, X, threshold=0.5): # implement the .predict() using the .predict_prob() method # X: input feature matrix of shape [N, d] # returns the prediction of shape [N, ], where each element is -1 or 1. # if the probability p>threshold, we determine t=1, otherwise t=-1

以下是您提供的代码的完整实现，包括_param()和get_param()方法的实现：

import numpy as np


def sigmoid(x):
    # the sigmoid function
    return 1 / (1 + np.exp(-x))


class LogisticReg(object):
    def __init__(self, indim=1):
        # initialize the parameters with all zeros
        # w: shape of [d+1, 1]
        self.w = np.zeros((indim + 1, 1))

    def set_param(self, weights, bias):
        # helper function to set the parameters
        # weights: vector of shape [d, ]
        # bias: scalar
        self.w[:-1] = weights.reshape(-1, 1)
        self.w[-1] = bias

    def get_param(self):
        # helper function to return the parameters
        # returns:
        # weights: vector of shape [d, ]
        # bias: scalar
        weights = self.w[:-1].reshape(-1)
        bias = self.w[-1]
        return weights, bias

    def compute_loss(self, X, t):
        # compute the loss
        # X: feature matrix of shape [N, d]
        # t: input label of shape [N, ]
        # NOTE: return the average of the log-likelihood, NOT the sum.

        # extend the input matrix
        X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1)

        # compute the log-likelihood
        z = X_ext @ self.w
        y = sigmoid(z)
        loss = -np.mean(t * np.log(y) + (1 - t) * np.log(1 - y))
        return loss

    def compute_grad(self, X, t):
        # X: feature matrix of shape [N, d]
        # grad: shape of [d, 1]
        # NOTE: return the average gradient, NOT the sum.
        X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1)
        z = X_ext @ self.w
        y = sigmoid(z)
        grad = np.mean(X_ext.T @ (y - t), axis=1)
        return grad

    def update(self, grad, lr=0.001):
        # update the weights
        # by the gradient descent rule
        self.w -= lr * grad.reshape(-1, 1)

    def fit(self, X, t, lr=0.001, max_iters=1000, eps=1e-7):
        # implement the .fit() using the gradient descent method.
        # args:
        #   X: input feature matrix of shape [N, d]
        #   t: input label of shape [N, ]
        #   lr: learning rate
        #   max_iters: maximum number of iterations
        #   eps: tolerance of the loss difference
        # TO NOTE:
        #   extend the input features before fitting to it.
        #   return the weight matrix of shape [indim+1, 1]
        X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1)
        prev_loss = np.inf
        for i in range(max_iters):
            loss = self.compute_loss(X, t)
            if np.abs(loss - prev_loss) < eps:
                break
            grad = self.compute_grad(X, t)
            self.update(grad, lr)
            prev_loss = loss
        return self.w

    def predict_prob(self, X):
        # implement the .predict_prob() using the parameters learned by .fit()
        # X: input feature matrix of shape [N, d]
        #   NOTE: make sure you extend the feature matrix first,
        #   the same way as what you did in .fit() method.
        # returns the prediction (likelihood) of shape [N, ]
        X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1)
        z = X_ext @ self.w
        y = sigmoid(z)
        return y

    def predict(self, X, threshold=0.5):
        # implement the .predict() using the .predict_prob() method
        # X: input feature matrix of shape [N, d]
        # returns the prediction of shape [N, ], where each element is -1 or 1.
        # if the probability p > threshold, we determine t = 1, otherwise t = -1
        prob = self.predict_prob(X)
        predictions = np.where(prob > threshold, 1, -1)
        return predictions

这个类实现了逻辑回归的基本功能，包括参数设置、计算损失、计算梯度、更新权重、拟合数据、预测概率和预测类别等方法。您可以使用这个类来拟合二分类问题的数据，并进行预测。